Abstract
The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before an outbreak occurs, it is often manually assessed and therefore labor intense, cost intensive and impracticable on a commercial scale. Recent advances of semi- and unsupervised convolutional neural networks (CNNs) have made them to the state of art technology for detecting anomalous behavior patterns in a variety of complex scene environments. In this study we apply such a CNN for anomaly detection to identify varying levels of activity in a multi-pen problem setup. By applying a two-stage approach we first trained the CNN to detect anomalies in the form of extreme activity behavior. Second, we trained a classifier to categorize the detected anomaly scores by learning the potential activity range of each pen. We evaluated our framework by analyzing 82 manually rated videos and achieved a success rate of 91%. Furthermore, we compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set. The results show the effectiveness of our framework, which can be applied without the need of a labor intense manual annotation process and can be utilized for the assessment of the pig activity in a variety of applications like early warning systems to detect changes in the state of health.
Highlights
The investigation of pig behavior is still challenging since specific activities like aggressive behavior of animals are highly complex and hard to predict [1]
We compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set
By employing our two-stage approach we show the applicability of semi-supervised machine learning techniques for the automatic determination of the activity level of piglets
Summary
The investigation of pig behavior is still challenging since specific activities like aggressive behavior of animals are highly complex and hard to predict [1]. A deviation from regular patterns may indicate stressful events like changes in the state of health, which could manifest themselves in form of behavior disorders [2,3,4] One of those major disorders is tail-biting, which reduces animal welfare [4,5] and simultaneously leads to an economic loss [5,6]. Due to the multifactorial origin of this abnormal behavior [7,8,9], tail-biting has so far been considered as unpredictable automatically [10] and difficult to detect by a single indicator To deal with this issue, several environmental and animal individual variables are used in early warning systems to detect tail-biting before an outbreak occurs [11]. The early detection of shifting points in the animal activity could provide crucial hints to further differentiate between e.g., aggressive and non-aggressive events
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